Big Data BizViz Restaurant Analytics

Copyright © 2016 BizViz Technologies Pvt. Ltd. www.bdbizviz.com
 Elements of Restaurant Analytics in the Big Data World
 Where is the Data for Analytics?
 What is required to get all these Analytics?
 Customer Analytics (Customer Life Cycle Value)
 Demand Forecasting, Feedback Analysis, RFM
 POS data Ad-Hoc Analysis (Self Service BI)
Agenda

Elements of Restaurant Analytics in Big Data World
 Customer Analytics
 Customer Lifetime Value
 Customer Sentiment Analytics
 Feedback Analytics – Polls, Survey
 Staff Analytics
 Productivity
 Operational Analytics
 POS Analytics
 Inventory Analytics
 Season based Analytics – Menu Analytics
 Weather Impact Analytics
 Sentiment Analytics
 Twitter Sentiments (real time feedback)
 Survey based Sentiments
 Restaurant Comparison Websites
 Branding- Marketing – Loyalty Program Analytics
 Gift Cards (Avg. Customer Spends 20% more than Gift Card Value)
 Loyalty Programs (Avg. Customer Spends 46% more to Businesses with Loyalty Programs)

Where is the Data?
 Structured (Inside the Business) – it tells you WHAT?
 POS (Revenue, Footfall, Customer Details)
 Suppliers (Inventory, Prices)
 Operational – Costs, Revenues, Margins
 Staff – Wages, Salaries, Tips, Productivity
* This data should be available with the Restaurant Chain
 Unstructured (Outside the Business) – it tells you WHY?
 Social Media – Likes, Trends, Tweets, Shares, Comments
 Customer Profiles and Loyalty Programs – Details, Preferences etc.
 Weather, Geographical and Traffic Patterns
* BizViz Social Media Browser can be Used to Pull this Data near Real Time

What is required to get all these Analytics
 Needed an Platform that is Vertically and Horizontally Scalable.
 The platform should be able to Integrate with E-Commerce Part of Restaurant Chain.
 The Platform should be able to Extract Data from POS System, Inventory, Internal Data, Twitter, Facebook,
Weather etc. all together, Seamless, near Real time.
 Platform has Cloud based access, Analytics is available on mobile devices
 Platform has ability to do Predictive Analysis on all this data together to find –
 Customer Patterns, Outliers, Correlations, Regressions, Classifications
 Strong Data Scientist Team is required to create Prescriptive Analytics
 Social Media Analytics – Sentiment Analytics using Real Time Messaging Service and Sentiment Engine to
Trap Emotions of Customers – Real time or from Websites rating different restaurants
 Ad Hoc Analysis capabilities – take the POS data and do Slice and Dice – Anyone should be able to do it.
 Strong UI and Visualization tools to give Advanced Analytics findings that are easy to understand
 Ability to take Polls, Surveys and Feedbacks from Customers on a real time basis
 Real Time Analytics, Push Analytics (triggers, Alerts), Pull Analytics
 Distributed deployment
BizViz Platform brings all these capabilities together in a seamless way

Customer Analytics (Customer Life Cycle Value)
 Using BizViz Predictive Analysis tool we performed customer segmentation with RFM & K-Means
model. This helps in understanding different Customers Life Cycle Value
Analytical Hierarchical Process is used to Segment the Customer in following types -
Best, Valuable, Shopper, First-Time, Churn, Frequent, Spenders, Uncertain

Feedback Analysis
Demand Forecasting
RFM
Demand Forecasting, Feedback Analysis, RFM

POS data Ad-Hoc Analysis (Self Service BI)
 Combine POS data with the Predictive output in BizViz “Business Story” (Self-Service BI) and translate that
story into actions. It helps in Engaging the Executive Team by explaining strategies and results more
powerfully.
 With self-service BI, Business users are able to track the important business parameters without depending
on their IT team..
 Business Story charts load seamlessly on Mobile app and are 100% responsive
 Doing Analysis on Business Story takes no time. Business Stories can be copied and shared with other users

Indicative Timeline to create these Analytics
Short Term – 15-30 Days
1. Cloud Account or Server Installation
2. Push POS data into Business Story Cube
to create Ad Hoc Analysis Charts
3. Real Time Sentiment Analytics of Your
Business from Twitter and Facebook
4. CXO level Descriptive Analytics on POS
data
5. Basic Survey and real time Survey
Analytics
Medium Term – 90-180 Days
1. ETL of Different Structured Sources and design of Data
Mart
2. Extending Business Story Charts with some Predictive
Analytics. Business Story on Mobile
3. CXO level dashboard with data from more than POS
databases
4. Extending Sentiment Charts to include sentiments from
public websites
Long Term – 240 - 365 Days (This requires clear requirements from business to be defined on time)
1. All types of Analytics mentioned in the main slides
2. Many CXO level Dashboards to give end to end KPIs of Business
3. All Key Predictive Analytics as described above
4. Big Data Analytics – Live streaming Data, IOT, Machine Learning data, Impact due to Weather etc.
5. From What to HOW -> Prescriptive Analytics
6. Adding more Analytics Workflows by creating internal Applications
7. Mobile Analytics and Mobile Apps implementation
8. Clustering and Complex Deployment for Vertical and Horizontal Scaling

A small sample Sentiment Analysis Report (Automated)
Note : The sample data that we took showed lot of Positive Feedback about Red Rooster

Thank You
For further information, please contact:
Avin Jain| Founder-CEO
Big Data BizViz LLC
Phone: (773)897-0939
e-mail: avin.jain@bdbizviz.com
Twitter: @bdbizviz

Big Data BizViz Restaurant Analytics

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